Triple
T242894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin Wall |
E4970
|
entity |
| Predicate | numberOfBorderGuards |
P9726
|
FINISHED |
| Object | thousands |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: thousands | Statement: [Berlin Wall, numberOfBorderGuards, thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBorderGuards Context triple: [Berlin Wall, numberOfBorderGuards, thousands]
-
A.
borderRegime
Indicates the type, rules, or control system governing how movement or interaction is managed across a border between entities.
-
B.
borderedBy
Indicates that one entity shares a common boundary or edge with another entity.
-
C.
hasBorderCrossing
Indicates that there exists a point or facility where movement or transit is possible between the boundaries of two adjacent regions or jurisdictions.
-
D.
countryBordering
Indicates that one country shares a land or maritime boundary directly with another country.
-
E.
borderWithUnitedStatesVia
Indicates that one entity shares a border with the United States specifically through or along the second entity (such as a body of water, territory, or region).
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b62839c8190824064fe5da6a92a |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25dcba5148190ab80fd14c7cf4bb4 |
completed | Feb. 28, 2026, 3:15 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.